Merge pull request #775 from FlowiseAI/feature/Vec2Doc
Feature/Vector to Prompt
This commit is contained in:
Henry Heng
GitHub
commit
e9adc15ff2
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@ -45,7 +45,8 @@ class ConversationChain_Chains implements INode {
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label: 'Document',
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name: 'document',
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type: 'Document',
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description: 'Include whole document into the context window',
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description:
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'Include whole document into the context window, if you get maximum context length error, please use model with higher context window like Claude 100k, or gpt4 32k',
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optional: true,
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list: true
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},
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@ -0,0 +1,87 @@
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import { VectorStore } from 'langchain/vectorstores/base'
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import { INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
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import { handleEscapeCharacters } from '../../../src/utils'
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class VectorStoreToDocument_DocumentLoaders implements INode {
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label: string
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name: string
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version: number
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description: string
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type: string
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icon: string
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category: string
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baseClasses: string[]
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inputs: INodeParams[]
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outputs: INodeOutputsValue[]
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constructor() {
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this.label = 'VectorStore To Document'
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this.name = 'vectorStoreToDocument'
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this.version = 1.0
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this.type = 'Document'
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this.icon = 'vectorretriever.svg'
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this.category = 'Document Loaders'
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this.description = 'Search documents with scores from vector store'
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this.baseClasses = [this.type]
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this.inputs = [
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{
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label: 'Vector Store',
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name: 'vectorStore',
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type: 'VectorStore'
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},
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{
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label: 'Minimum Score (%)',
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name: 'minScore',
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type: 'number',
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optional: true,
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placeholder: '75',
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step: 1,
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description: 'Minumum score for embeddings documents to be included'
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}
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]
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this.outputs = [
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{
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label: 'Document',
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name: 'document',
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baseClasses: this.baseClasses
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},
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{
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label: 'Text',
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name: 'text',
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baseClasses: ['string', 'json']
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}
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]
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}
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async init(nodeData: INodeData, input: string): Promise<any> {
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const vectorStore = nodeData.inputs?.vectorStore as VectorStore
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const minScore = nodeData.inputs?.minScore as number
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const output = nodeData.outputs?.output as string
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const topK = (vectorStore as any)?.k ?? 4
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const docs = await vectorStore.similaritySearchWithScore(input, topK)
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// eslint-disable-next-line no-console
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console.log('\x1b[94m\x1b[1m\n*****VectorStore Documents*****\n\x1b[0m\x1b[0m')
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// eslint-disable-next-line no-console
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console.log(docs)
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if (output === 'document') {
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let finaldocs = []
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for (const doc of docs) {
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if (minScore && doc[1] < minScore / 100) continue
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finaldocs.push(doc[0])
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}
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return finaldocs
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} else {
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let finaltext = ''
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for (const doc of docs) {
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if (minScore && doc[1] < minScore / 100) continue
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finaltext += `${doc[0].pageContent}\n`
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}
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return handleEscapeCharacters(finaltext, false)
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}
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}
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}
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module.exports = { nodeClass: VectorStoreToDocument_DocumentLoaders }
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@ -0,0 +1,7 @@
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<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-file-database" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
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<path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
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<path d="M12 12.75m-4 0a4 1.75 0 1 0 8 0a4 1.75 0 1 0 -8 0"></path>
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<path d="M8 12.5v3.75c0 .966 1.79 1.75 4 1.75s4 -.784 4 -1.75v-3.75"></path>
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<path d="M14 3v4a1 1 0 0 0 1 1h4"></path>
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<path d="M17 21h-10a2 2 0 0 1 -2 -2v-14a2 2 0 0 1 2 -2h7l5 5v11a2 2 0 0 1 -2 2z"></path>
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</svg>
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After Width: | Height: | Size: 588 B |
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@ -520,3 +520,22 @@ export const mapChatHistory = (options: ICommonObject): ChatMessageHistory => {
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}
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return new ChatMessageHistory(chatHistory)
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}
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/**
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* Convert incoming chat history to string
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* @param {IMessage[]} chatHistory
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* @returns {string}
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*/
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export const convertChatHistoryToText = (chatHistory: IMessage[]): string => {
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return chatHistory
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.map((chatMessage) => {
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if (chatMessage.type === 'apiMessage') {
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return `Assistant: ${chatMessage.message}`
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} else if (chatMessage.type === 'userMessage') {
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return `Human: ${chatMessage.message}`
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} else {
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return `${chatMessage.message}`
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}
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})
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.join('\n')
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}
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@ -0,0 +1,966 @@
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{
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"description": "Use chat history to rephrase user question, and answer the rephrased question using retrieved docs from vector store",
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"nodes": [
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{
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"width": 300,
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"height": 503,
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"id": "pineconeExistingIndex_0",
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"position": {
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"x": 1062.7418678410986,
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"y": -109.27680365777141
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},
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"type": "customNode",
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"data": {
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"id": "pineconeExistingIndex_0",
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"label": "Pinecone Load Existing Index",
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"version": 1,
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"name": "pineconeExistingIndex",
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"type": "Pinecone",
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"baseClasses": ["Pinecone", "VectorStoreRetriever", "BaseRetriever"],
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"category": "Vector Stores",
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"description": "Load existing index from Pinecone (i.e: Document has been upserted)",
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"inputParams": [
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{
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"label": "Connect Credential",
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"name": "credential",
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"type": "credential",
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"credentialNames": ["pineconeApi"],
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"id": "pineconeExistingIndex_0-input-credential-credential"
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},
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{
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"label": "Pinecone Index",
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"name": "pineconeIndex",
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"type": "string",
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"id": "pineconeExistingIndex_0-input-pineconeIndex-string"
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},
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{
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"label": "Pinecone Namespace",
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"name": "pineconeNamespace",
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"type": "string",
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"placeholder": "my-first-namespace",
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"additionalParams": true,
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"optional": true,
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"id": "pineconeExistingIndex_0-input-pineconeNamespace-string"
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},
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{
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"label": "Pinecone Metadata Filter",
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"name": "pineconeMetadataFilter",
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"type": "json",
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"optional": true,
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"additionalParams": true,
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"id": "pineconeExistingIndex_0-input-pineconeMetadataFilter-json"
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},
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{
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"label": "Top K",
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"name": "topK",
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"description": "Number of top results to fetch. Default to 4",
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"placeholder": "4",
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"type": "number",
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"additionalParams": true,
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"optional": true,
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"id": "pineconeExistingIndex_0-input-topK-number"
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}
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],
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"inputAnchors": [
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{
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"label": "Embeddings",
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"name": "embeddings",
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"type": "Embeddings",
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"id": "pineconeExistingIndex_0-input-embeddings-Embeddings"
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}
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],
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"inputs": {
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"embeddings": "{{openAIEmbeddings_0.data.instance}}",
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"pineconeIndex": "newindex",
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"pineconeNamespace": "",
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"pineconeMetadataFilter": "{}",
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"topK": ""
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},
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"outputAnchors": [
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{
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"name": "output",
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"label": "Output",
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"type": "options",
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"options": [
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{
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"id": "pineconeExistingIndex_0-output-retriever-Pinecone|VectorStoreRetriever|BaseRetriever",
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"name": "retriever",
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"label": "Pinecone Retriever",
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"type": "Pinecone | VectorStoreRetriever | BaseRetriever"
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},
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{
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"id": "pineconeExistingIndex_0-output-vectorStore-Pinecone|VectorStore",
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"name": "vectorStore",
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"label": "Pinecone Vector Store",
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"type": "Pinecone | VectorStore"
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}
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],
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"default": "retriever"
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}
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],
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"outputs": {
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"output": "vectorStore"
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},
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"selected": false
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},
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"selected": false,
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"positionAbsolute": {
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"x": 1062.7418678410986,
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"y": -109.27680365777141
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},
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"dragging": false
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},
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{
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"width": 300,
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"height": 327,
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"id": "openAIEmbeddings_0",
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"position": {
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"x": 711.3971966563331,
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"y": 7.7184225021727
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},
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"type": "customNode",
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"data": {
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"id": "openAIEmbeddings_0",
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"label": "OpenAI Embeddings",
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"version": 1,
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"name": "openAIEmbeddings",
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"type": "OpenAIEmbeddings",
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"baseClasses": ["OpenAIEmbeddings", "Embeddings"],
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"category": "Embeddings",
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"description": "OpenAI API to generate embeddings for a given text",
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"inputParams": [
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{
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"label": "Connect Credential",
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"name": "credential",
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"type": "credential",
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"credentialNames": ["openAIApi"],
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"id": "openAIEmbeddings_0-input-credential-credential"
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},
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{
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"label": "Strip New Lines",
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"name": "stripNewLines",
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"type": "boolean",
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"optional": true,
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"additionalParams": true,
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"id": "openAIEmbeddings_0-input-stripNewLines-boolean"
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},
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{
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"label": "Batch Size",
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"name": "batchSize",
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"type": "number",
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"optional": true,
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"additionalParams": true,
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"id": "openAIEmbeddings_0-input-batchSize-number"
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},
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{
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"label": "Timeout",
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"name": "timeout",
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"type": "number",
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"optional": true,
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"additionalParams": true,
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"id": "openAIEmbeddings_0-input-timeout-number"
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},
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{
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"label": "BasePath",
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"name": "basepath",
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"type": "string",
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"optional": true,
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"additionalParams": true,
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"id": "openAIEmbeddings_0-input-basepath-string"
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}
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],
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"inputAnchors": [],
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"inputs": {
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"stripNewLines": "",
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"batchSize": "",
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"timeout": "",
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"basepath": ""
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},
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"outputAnchors": [
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{
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"id": "openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings",
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"name": "openAIEmbeddings",
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"label": "OpenAIEmbeddings",
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"type": "OpenAIEmbeddings | Embeddings"
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}
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],
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"outputs": {},
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"selected": false
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},
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"selected": false,
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"positionAbsolute": {
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"x": 711.3971966563331,
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"y": 7.7184225021727
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},
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"dragging": false
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},
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{
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"width": 300,
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"height": 473,
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"id": "promptTemplate_0",
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"position": {
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"x": 348.2881107399286,
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"y": -97.74510214137423
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},
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"type": "customNode",
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"data": {
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"id": "promptTemplate_0",
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"label": "Prompt Template",
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"version": 1,
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"name": "promptTemplate",
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"type": "PromptTemplate",
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"baseClasses": ["PromptTemplate", "BaseStringPromptTemplate", "BasePromptTemplate", "Runnable"],
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"category": "Prompts",
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"description": "Schema to represent a basic prompt for an LLM",
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"inputParams": [
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{
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"label": "Template",
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"name": "template",
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"type": "string",
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"rows": 4,
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"placeholder": "What is a good name for a company that makes {product}?",
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"id": "promptTemplate_0-input-template-string"
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},
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{
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"label": "Format Prompt Values",
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"name": "promptValues",
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"type": "json",
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"optional": true,
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"acceptVariable": true,
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"list": true,
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"id": "promptTemplate_0-input-promptValues-json"
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}
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],
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"inputAnchors": [],
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"inputs": {
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"template": "Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.\n\nChat History:\n{chat_history}\nFollow Up Input: {question}\nStandalone question:",
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"promptValues": "{\"question\":\"{{question}}\",\"chat_history\":\"{{chat_history}}\"}"
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},
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"outputAnchors": [
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{
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"id": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable",
|
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"name": "promptTemplate",
|
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"label": "PromptTemplate",
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"type": "PromptTemplate | BaseStringPromptTemplate | BasePromptTemplate | Runnable"
|
||||
}
|
||||
],
|
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"outputs": {},
|
||||
"selected": false
|
||||
},
|
||||
"selected": false,
|
||||
"positionAbsolute": {
|
||||
"x": 348.2881107399286,
|
||||
"y": -97.74510214137423
|
||||
},
|
||||
"dragging": false
|
||||
},
|
||||
{
|
||||
"width": 300,
|
||||
"height": 522,
|
||||
"id": "chatOpenAI_0",
|
||||
"position": {
|
||||
"x": 335.7621848973805,
|
||||
"y": -651.7411273245009
|
||||
},
|
||||
"type": "customNode",
|
||||
"data": {
|
||||
"id": "chatOpenAI_0",
|
||||
"label": "ChatOpenAI",
|
||||
"version": 1,
|
||||
"name": "chatOpenAI",
|
||||
"type": "ChatOpenAI",
|
||||
"baseClasses": ["ChatOpenAI", "BaseChatModel", "BaseLanguageModel", "Runnable"],
|
||||
"category": "Chat Models",
|
||||
"description": "Wrapper around OpenAI large language models that use the Chat endpoint",
|
||||
"inputParams": [
|
||||
{
|
||||
"label": "Connect Credential",
|
||||
"name": "credential",
|
||||
"type": "credential",
|
||||
"credentialNames": ["openAIApi"],
|
||||
"id": "chatOpenAI_0-input-credential-credential"
|
||||
},
|
||||
{
|
||||
"label": "Model Name",
|
||||
"name": "modelName",
|
||||
"type": "options",
|
||||
"options": [
|
||||
{
|
||||
"label": "gpt-4",
|
||||
"name": "gpt-4"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-0613",
|
||||
"name": "gpt-4-0613"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-32k",
|
||||
"name": "gpt-4-32k"
|
||||
},
|
||||
{
|
||||
"label": "gpt-4-32k-0613",
|
||||
"name": "gpt-4-32k-0613"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo",
|
||||
"name": "gpt-3.5-turbo"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-0613",
|
||||
"name": "gpt-3.5-turbo-0613"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-16k",
|
||||
"name": "gpt-3.5-turbo-16k"
|
||||
},
|
||||
{
|
||||
"label": "gpt-3.5-turbo-16k-0613",
|
||||
"name": "gpt-3.5-turbo-16k-0613"
|
||||
}
|
||||
],
|
||||
"default": "gpt-3.5-turbo",
|
||||
"optional": true,
|
||||
"id": "chatOpenAI_0-input-modelName-options"
|
||||
},
|
||||
{
|
||||
"label": "Temperature",
|
||||
"name": "temperature",
|
||||
"type": "number",
|
||||
"step": 0.1,
|
||||
"default": 0.9,
|
||||
"optional": true,
|
||||
"id": "chatOpenAI_0-input-temperature-number"
|
||||
},
|
||||
{
|
||||
"label": "Max Tokens",
|
||||
"name": "maxTokens",
|
||||
"type": "number",
|
||||
"step": 1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-maxTokens-number"
|
||||
},
|
||||
{
|
||||
"label": "Top Probability",
|
||||
"name": "topP",
|
||||
"type": "number",
|
||||
"step": 0.1,
|
||||
"optional": true,
|
||||
"additionalParams": true,
|
||||
"id": "chatOpenAI_0-input-topP-number"
|
||||
},
|
||||
{
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|
||||
"x": 1407.7038120189868,
|
||||
"y": -26.16468811205081
|
||||
},
|
||||
"dragging": false
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": "openAIEmbeddings_0",
|
||||
"sourceHandle": "openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings",
|
||||
"target": "pineconeExistingIndex_0",
|
||||
"targetHandle": "pineconeExistingIndex_0-input-embeddings-Embeddings",
|
||||
"type": "buttonedge",
|
||||
"id": "openAIEmbeddings_0-openAIEmbeddings_0-output-openAIEmbeddings-OpenAIEmbeddings|Embeddings-pineconeExistingIndex_0-pineconeExistingIndex_0-input-embeddings-Embeddings",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "pineconeExistingIndex_0",
|
||||
"sourceHandle": "pineconeExistingIndex_0-output-vectorStore-Pinecone|VectorStore",
|
||||
"target": "vectorStoreToDocument_0",
|
||||
"targetHandle": "vectorStoreToDocument_0-input-vectorStore-VectorStore",
|
||||
"type": "buttonedge",
|
||||
"id": "pineconeExistingIndex_0-pineconeExistingIndex_0-output-vectorStore-Pinecone|VectorStore-vectorStoreToDocument_0-vectorStoreToDocument_0-input-vectorStore-VectorStore",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "vectorStoreToDocument_0",
|
||||
"sourceHandle": "vectorStoreToDocument_0-output-text-string|json",
|
||||
"target": "promptTemplate_1",
|
||||
"targetHandle": "promptTemplate_1-input-promptValues-json",
|
||||
"type": "buttonedge",
|
||||
"id": "vectorStoreToDocument_0-vectorStoreToDocument_0-output-text-string|json-promptTemplate_1-promptTemplate_1-input-promptValues-json",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "chatOpenAI_0",
|
||||
"sourceHandle": "chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
|
||||
"target": "llmChain_0",
|
||||
"targetHandle": "llmChain_0-input-model-BaseLanguageModel",
|
||||
"type": "buttonedge",
|
||||
"id": "chatOpenAI_0-chatOpenAI_0-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-llmChain_0-llmChain_0-input-model-BaseLanguageModel",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "promptTemplate_0",
|
||||
"sourceHandle": "promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable",
|
||||
"target": "llmChain_0",
|
||||
"targetHandle": "llmChain_0-input-prompt-BasePromptTemplate",
|
||||
"type": "buttonedge",
|
||||
"id": "promptTemplate_0-promptTemplate_0-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable-llmChain_0-llmChain_0-input-prompt-BasePromptTemplate",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "llmChain_0",
|
||||
"sourceHandle": "llmChain_0-output-outputPrediction-string|json",
|
||||
"target": "promptTemplate_1",
|
||||
"targetHandle": "promptTemplate_1-input-promptValues-json",
|
||||
"type": "buttonedge",
|
||||
"id": "llmChain_0-llmChain_0-output-outputPrediction-string|json-promptTemplate_1-promptTemplate_1-input-promptValues-json",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "chatOpenAI_1",
|
||||
"sourceHandle": "chatOpenAI_1-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable",
|
||||
"target": "llmChain_1",
|
||||
"targetHandle": "llmChain_1-input-model-BaseLanguageModel",
|
||||
"type": "buttonedge",
|
||||
"id": "chatOpenAI_1-chatOpenAI_1-output-chatOpenAI-ChatOpenAI|BaseChatModel|BaseLanguageModel|Runnable-llmChain_1-llmChain_1-input-model-BaseLanguageModel",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
},
|
||||
{
|
||||
"source": "promptTemplate_1",
|
||||
"sourceHandle": "promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable",
|
||||
"target": "llmChain_1",
|
||||
"targetHandle": "llmChain_1-input-prompt-BasePromptTemplate",
|
||||
"type": "buttonedge",
|
||||
"id": "promptTemplate_1-promptTemplate_1-output-promptTemplate-PromptTemplate|BaseStringPromptTemplate|BasePromptTemplate|Runnable-llmChain_1-llmChain_1-input-prompt-BasePromptTemplate",
|
||||
"data": {
|
||||
"label": ""
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -811,11 +811,17 @@ export class App {
|
|||
}
|
||||
}
|
||||
|
||||
/*** Get chatflows and prepare data ***/
|
||||
const flowData = chatflow.flowData
|
||||
const parsedFlowData: IReactFlowObject = JSON.parse(flowData)
|
||||
const nodes = parsedFlowData.nodes
|
||||
const edges = parsedFlowData.edges
|
||||
|
||||
/* Reuse the flow without having to rebuild (to avoid duplicated upsert, recomputation) when all these conditions met:
|
||||
* - Node Data already exists in pool
|
||||
* - Still in sync (i.e the flow has not been modified since)
|
||||
* - Existing overrideConfig and new overrideConfig are the same
|
||||
* - Flow doesn't start with nodes that depend on incomingInput.question
|
||||
* - Flow doesn't start with/contain nodes that depend on incomingInput.question
|
||||
***/
|
||||
const isFlowReusable = () => {
|
||||
return (
|
||||
|
|
@ -826,16 +832,10 @@ export class App {
|
|||
this.chatflowPool.activeChatflows[chatflowid].overrideConfig,
|
||||
incomingInput.overrideConfig
|
||||
) &&
|
||||
!isStartNodeDependOnInput(this.chatflowPool.activeChatflows[chatflowid].startingNodes)
|
||||
!isStartNodeDependOnInput(this.chatflowPool.activeChatflows[chatflowid].startingNodes, nodes)
|
||||
)
|
||||
}
|
||||
|
||||
/*** Get chatflows and prepare data ***/
|
||||
const flowData = chatflow.flowData
|
||||
const parsedFlowData: IReactFlowObject = JSON.parse(flowData)
|
||||
const nodes = parsedFlowData.nodes
|
||||
const edges = parsedFlowData.edges
|
||||
|
||||
if (isFlowReusable()) {
|
||||
nodeToExecuteData = this.chatflowPool.activeChatflows[chatflowid].endingNodeData
|
||||
isStreamValid = isFlowValidForStream(nodes, nodeToExecuteData)
|
||||
|
|
@ -884,6 +884,7 @@ export class App {
|
|||
depthQueue,
|
||||
this.nodesPool.componentNodes,
|
||||
incomingInput.question,
|
||||
incomingInput.history,
|
||||
chatId,
|
||||
this.AppDataSource,
|
||||
incomingInput?.overrideConfig
|
||||
|
|
@ -894,7 +895,12 @@ export class App {
|
|||
|
||||
if (incomingInput.overrideConfig)
|
||||
nodeToExecute.data = replaceInputsWithConfig(nodeToExecute.data, incomingInput.overrideConfig)
|
||||
const reactFlowNodeData: INodeData = resolveVariables(nodeToExecute.data, reactFlowNodes, incomingInput.question)
|
||||
const reactFlowNodeData: INodeData = resolveVariables(
|
||||
nodeToExecute.data,
|
||||
reactFlowNodes,
|
||||
incomingInput.question,
|
||||
incomingInput.history
|
||||
)
|
||||
nodeToExecuteData = reactFlowNodeData
|
||||
|
||||
const startingNodes = nodes.filter((nd) => startingNodeIds.includes(nd.id))
|
||||
|
|
|
|||
|
|
@ -19,7 +19,14 @@ import {
|
|||
ICredentialReqBody
|
||||
} from '../Interface'
|
||||
import { cloneDeep, get, omit, merge, isEqual } from 'lodash'
|
||||
import { ICommonObject, getInputVariables, IDatabaseEntity, handleEscapeCharacters } from 'flowise-components'
|
||||
import {
|
||||
ICommonObject,
|
||||
getInputVariables,
|
||||
IDatabaseEntity,
|
||||
handleEscapeCharacters,
|
||||
IMessage,
|
||||
convertChatHistoryToText
|
||||
} from 'flowise-components'
|
||||
import { scryptSync, randomBytes, timingSafeEqual } from 'crypto'
|
||||
import { lib, PBKDF2, AES, enc } from 'crypto-js'
|
||||
|
||||
|
|
@ -30,6 +37,7 @@ import { Tool } from '../entity/Tool'
|
|||
import { DataSource } from 'typeorm'
|
||||
|
||||
const QUESTION_VAR_PREFIX = 'question'
|
||||
const CHAT_HISTORY_VAR_PREFIX = 'chat_history'
|
||||
const REDACTED_CREDENTIAL_VALUE = '_FLOWISE_BLANK_07167752-1a71-43b1-bf8f-4f32252165db'
|
||||
|
||||
export const databaseEntities: IDatabaseEntity = { ChatFlow: ChatFlow, ChatMessage: ChatMessage, Tool: Tool, Credential: Credential }
|
||||
|
|
@ -199,6 +207,7 @@ export const buildLangchain = async (
|
|||
depthQueue: IDepthQueue,
|
||||
componentNodes: IComponentNodes,
|
||||
question: string,
|
||||
chatHistory: IMessage[],
|
||||
chatId: string,
|
||||
appDataSource: DataSource,
|
||||
overrideConfig?: ICommonObject
|
||||
|
|
@ -231,7 +240,7 @@ export const buildLangchain = async (
|
|||
|
||||
let flowNodeData = cloneDeep(reactFlowNode.data)
|
||||
if (overrideConfig) flowNodeData = replaceInputsWithConfig(flowNodeData, overrideConfig)
|
||||
const reactFlowNodeData: INodeData = resolveVariables(flowNodeData, flowNodes, question)
|
||||
const reactFlowNodeData: INodeData = resolveVariables(flowNodeData, flowNodes, question, chatHistory)
|
||||
|
||||
logger.debug(`[server]: Initializing ${reactFlowNode.data.label} (${reactFlowNode.data.id})`)
|
||||
flowNodes[nodeIndex].data.instance = await newNodeInstance.init(reactFlowNodeData, question, {
|
||||
|
|
@ -315,7 +324,13 @@ export const clearSessionMemory = async (
|
|||
* @param {boolean} isAcceptVariable
|
||||
* @returns {string}
|
||||
*/
|
||||
export const getVariableValue = (paramValue: string, reactFlowNodes: IReactFlowNode[], question: string, isAcceptVariable = false) => {
|
||||
export const getVariableValue = (
|
||||
paramValue: string,
|
||||
reactFlowNodes: IReactFlowNode[],
|
||||
question: string,
|
||||
chatHistory: IMessage[],
|
||||
isAcceptVariable = false
|
||||
) => {
|
||||
let returnVal = paramValue
|
||||
const variableStack = []
|
||||
const variableDict = {} as IVariableDict
|
||||
|
|
@ -345,6 +360,10 @@ export const getVariableValue = (paramValue: string, reactFlowNodes: IReactFlowN
|
|||
variableDict[`{{${variableFullPath}}}`] = handleEscapeCharacters(question, false)
|
||||
}
|
||||
|
||||
if (isAcceptVariable && variableFullPath === CHAT_HISTORY_VAR_PREFIX) {
|
||||
variableDict[`{{${variableFullPath}}}`] = handleEscapeCharacters(convertChatHistoryToText(chatHistory), false)
|
||||
}
|
||||
|
||||
// Split by first occurrence of '.' to get just nodeId
|
||||
const [variableNodeId, _] = variableFullPath.split('.')
|
||||
const executedNode = reactFlowNodes.find((nd) => nd.id === variableNodeId)
|
||||
|
|
@ -400,7 +419,12 @@ export const isVectorStoreFaiss = (flowNodeData: INodeData) => {
|
|||
* @param {string} question
|
||||
* @returns {INodeData}
|
||||
*/
|
||||
export const resolveVariables = (reactFlowNodeData: INodeData, reactFlowNodes: IReactFlowNode[], question: string): INodeData => {
|
||||
export const resolveVariables = (
|
||||
reactFlowNodeData: INodeData,
|
||||
reactFlowNodes: IReactFlowNode[],
|
||||
question: string,
|
||||
chatHistory: IMessage[]
|
||||
): INodeData => {
|
||||
let flowNodeData = cloneDeep(reactFlowNodeData)
|
||||
if (reactFlowNodeData.instance && isVectorStoreFaiss(reactFlowNodeData)) {
|
||||
// omit and merge because cloneDeep of instance gives "Illegal invocation" Exception
|
||||
|
|
@ -415,13 +439,13 @@ export const resolveVariables = (reactFlowNodeData: INodeData, reactFlowNodes: I
|
|||
if (Array.isArray(paramValue)) {
|
||||
const resolvedInstances = []
|
||||
for (const param of paramValue) {
|
||||
const resolvedInstance = getVariableValue(param, reactFlowNodes, question)
|
||||
const resolvedInstance = getVariableValue(param, reactFlowNodes, question, chatHistory)
|
||||
resolvedInstances.push(resolvedInstance)
|
||||
}
|
||||
paramsObj[key] = resolvedInstances
|
||||
} else {
|
||||
const isAcceptVariable = reactFlowNodeData.inputParams.find((param) => param.name === key)?.acceptVariable ?? false
|
||||
const resolvedInstance = getVariableValue(paramValue, reactFlowNodes, question, isAcceptVariable)
|
||||
const resolvedInstance = getVariableValue(paramValue, reactFlowNodes, question, chatHistory, isAcceptVariable)
|
||||
paramsObj[key] = resolvedInstance
|
||||
}
|
||||
}
|
||||
|
|
@ -474,13 +498,17 @@ export const replaceInputsWithConfig = (flowNodeData: INodeData, overrideConfig:
|
|||
* @param {IReactFlowNode[]} startingNodes
|
||||
* @returns {boolean}
|
||||
*/
|
||||
export const isStartNodeDependOnInput = (startingNodes: IReactFlowNode[]): boolean => {
|
||||
export const isStartNodeDependOnInput = (startingNodes: IReactFlowNode[], nodes: IReactFlowNode[]): boolean => {
|
||||
for (const node of startingNodes) {
|
||||
for (const inputName in node.data.inputs) {
|
||||
const inputVariables = getInputVariables(node.data.inputs[inputName])
|
||||
if (inputVariables.length > 0) return true
|
||||
}
|
||||
}
|
||||
const whitelistNodeNames = ['vectorStoreToDocument']
|
||||
for (const node of nodes) {
|
||||
if (whitelistNodeNames.includes(node.data.name)) return true
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
|
|
|
|||
Binary file not shown.
|
After Width: | Height: | Size: 12 KiB |
|
|
@ -123,7 +123,7 @@ const NodeInfoDialog = ({ show, dialogProps, onCancel }) => {
|
|||
</div>
|
||||
)}
|
||||
{getNodeConfigApi.data && getNodeConfigApi.data.length > 0 && (
|
||||
<TableViewOnly rows={getNodeConfigApi.data} columns={Object.keys(getNodeConfigApi.data[0])} />
|
||||
<TableViewOnly rows={getNodeConfigApi.data} columns={Object.keys(getNodeConfigApi.data[0]).slice(-3)} />
|
||||
)}
|
||||
</DialogContent>
|
||||
</Dialog>
|
||||
|
|
|
|||
|
|
@ -2,14 +2,15 @@ import { useSelector } from 'react-redux'
|
|||
import PropTypes from 'prop-types'
|
||||
import { Box, List, ListItemButton, ListItem, ListItemAvatar, ListItemText, Typography, Stack } from '@mui/material'
|
||||
import PerfectScrollbar from 'react-perfect-scrollbar'
|
||||
|
||||
import robotPNG from 'assets/images/robot.png'
|
||||
import chatPNG from 'assets/images/chathistory.png'
|
||||
import { baseURL } from 'store/constant'
|
||||
|
||||
const SelectVariable = ({ availableNodesForVariable, disabled = false, onSelectAndReturnVal }) => {
|
||||
const customization = useSelector((state) => state.customization)
|
||||
|
||||
const onSelectOutputResponseClick = (node, isUserQuestion = false) => {
|
||||
let variablePath = isUserQuestion ? `question` : `${node.id}.data.instance`
|
||||
const onSelectOutputResponseClick = (node, prefix) => {
|
||||
let variablePath = node ? `${node.id}.data.instance` : prefix
|
||||
const newInput = `{{${variablePath}}}`
|
||||
onSelectAndReturnVal(newInput)
|
||||
}
|
||||
|
|
@ -32,7 +33,7 @@ const SelectVariable = ({ availableNodesForVariable, disabled = false, onSelectA
|
|||
mb: 1
|
||||
}}
|
||||
disabled={disabled}
|
||||
onClick={() => onSelectOutputResponseClick(null, true)}
|
||||
onClick={() => onSelectOutputResponseClick(null, 'question')}
|
||||
>
|
||||
<ListItem alignItems='center'>
|
||||
<ListItemAvatar>
|
||||
|
|
@ -52,13 +53,52 @@ const SelectVariable = ({ availableNodesForVariable, disabled = false, onSelectA
|
|||
objectFit: 'contain'
|
||||
}}
|
||||
alt='AI'
|
||||
src='https://raw.githubusercontent.com/zahidkhawaja/langchain-chat-nextjs/main/public/parroticon.png'
|
||||
src={robotPNG}
|
||||
/>
|
||||
</div>
|
||||
</ListItemAvatar>
|
||||
<ListItemText sx={{ ml: 1 }} primary='question' secondary={`User's question from chatbox`} />
|
||||
</ListItem>
|
||||
</ListItemButton>
|
||||
<ListItemButton
|
||||
sx={{
|
||||
p: 0,
|
||||
borderRadius: `${customization.borderRadius}px`,
|
||||
boxShadow: '0 2px 14px 0 rgb(32 40 45 / 8%)',
|
||||
mb: 1
|
||||
}}
|
||||
disabled={disabled}
|
||||
onClick={() => onSelectOutputResponseClick(null, 'chat_history')}
|
||||
>
|
||||
<ListItem alignItems='center'>
|
||||
<ListItemAvatar>
|
||||
<div
|
||||
style={{
|
||||
width: 50,
|
||||
height: 50,
|
||||
borderRadius: '50%',
|
||||
backgroundColor: 'white'
|
||||
}}
|
||||
>
|
||||
<img
|
||||
style={{
|
||||
width: '100%',
|
||||
height: '100%',
|
||||
padding: 10,
|
||||
objectFit: 'contain'
|
||||
}}
|
||||
alt='chatHistory'
|
||||
src={chatPNG}
|
||||
/>
|
||||
</div>
|
||||
</ListItemAvatar>
|
||||
<ListItemText
|
||||
sx={{ ml: 1 }}
|
||||
primary='chat_history'
|
||||
secondary={`Past conversation history between user and AI`}
|
||||
/>
|
||||
</ListItem>
|
||||
</ListItemButton>
|
||||
{availableNodesForVariable &&
|
||||
availableNodesForVariable.length > 0 &&
|
||||
availableNodesForVariable.map((node, index) => {
|
||||
|
|
|
|||
Loading…
Reference in New Issue